Different sets of parameters in the ModelComparisonSimulator #441
Description
This bug was reported in this Discourse thread, thanks to dizyd for reporting. The ModelComparisonSimulator throws an error when different prior parameters are specified for the different simulators. The minimal reproducible example is:
import bayesflow as bf import numpy as np rng = np.random.default_rng() def prior_1(): return dict(w=rng.uniform()) def prior_2(): return dict(c=rng.uniform()) def model_1(w): return dict(x = w) def model_2(c): return dict(x = c) simulator_1 = bf.make_simulator([prior_1,model_1]) simulator_2 = bf.make_simulator([prior_2,model_2]) simulator = bf.simulators.ModelComparisonSimulator(simulators=[simulator_1, simulator_2]) simulator.sample(10)The line that throws the error is sims = tree_concatenate(sims, numpy=True).
I'm not familiar with that code, maybe @Kucharssim, @stefanradev93 or @LarsKue can take a look at it.
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